Overview

Dataset statistics

Number of variables52
Number of observations16947
Missing cells18150
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory223.0 B

Variable types

CAT32
NUM9
DATE6
BOOL5

Warnings

Submitted_for_Approval has constant value "16947" Constant
Last_Activity has constant value "16947" Constant
ASP_(converted)_Currency has constant value "16947" Constant
Actual_Delivery_Date has constant value "16947" Constant
Prod_Category_A has constant value "16947" Constant
Territory has a high cardinality: 76 distinct values High cardinality
Billing_Country has a high cardinality: 80 distinct values High cardinality
Account_Name has a high cardinality: 1635 distinct values High cardinality
Opportunity_Name has a high cardinality: 9841 distinct values High cardinality
Opportunity_Owner has a high cardinality: 53 distinct values High cardinality
Price has a high cardinality: 53 distinct values High cardinality
Last_Modified_By has a high cardinality: 55 distinct values High cardinality
Product_Family has a high cardinality: 227 distinct values High cardinality
Product_Name has a high cardinality: 456 distinct values High cardinality
Month has a high cardinality: 53 distinct values High cardinality
Delivery_Year is highly correlated with Sales_Contract_NoHigh correlation
Sales_Contract_No is highly correlated with Delivery_YearHigh correlation
Territory is highly correlated with RegionHigh correlation
Region is highly correlated with Territory and 3 other fieldsHigh correlation
Billing_Country is highly correlated with RegionHigh correlation
Account_Owner is highly correlated with RegionHigh correlation
Opportunity_Owner is highly correlated with RegionHigh correlation
Delivery_Quarter is highly correlated with MonthHigh correlation
Month is highly correlated with Delivery_QuarterHigh correlation
Total_Amount_Currency is highly correlated with ASP_Currency and 1 other fieldsHigh correlation
ASP_Currency is highly correlated with Total_Amount_Currency and 1 other fieldsHigh correlation
Total_Taxable_Amount_Currency is highly correlated with ASP_Currency and 1 other fieldsHigh correlation
Sales_Contract_No has 6973 (41.1%) missing values Missing
Quote_Expiry_Date has 4625 (27.3%) missing values Missing
ASP has 3209 (18.9%) missing values Missing
ASP_(converted) has 3209 (18.9%) missing values Missing
ASP_(converted) is highly skewed (γ1 = 80.47407411) Skewed
Total_Amount is highly skewed (γ1 = 42.00442155) Skewed
Total_Taxable_Amount is highly skewed (γ1 = 33.59918963) Skewed
ID has unique values Unique
ASP has 301 (1.8%) zeros Zeros
ASP_(converted) has 300 (1.8%) zeros Zeros
TRF has 11971 (70.6%) zeros Zeros
Total_Taxable_Amount has 997 (5.9%) zeros Zeros

Reproduction

Analysis started2020-10-24 14:33:06.555382
Analysis finished2020-10-24 14:33:39.544073
Duration32.99 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

ID
Real number (ℝ≥0)

UNIQUE

Distinct16947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17666.65044
Minimum4854
Maximum28773
Zeros0
Zeros (%)0.0%
Memory size132.4 KiB
2020-10-24T11:33:39.623234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4854
5-th percentile6095.3
Q111953
median18114
Q323845.5
95-th percentile27687.4
Maximum28773
Range23919
Interquartile range (IQR)11892.5

Descriptive statistics

Standard deviation6940.859372
Coefficient of variation (CV)0.3928791933
Kurtosis-1.172033787
Mean17666.65044
Median Absolute Deviation (MAD)5914
Skewness-0.1785736993
Sum299396725
Variance48175528.83
MonotocityNot monotonic
2020-10-24T11:33:39.745255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
61411< 0.1%
 
273841< 0.1%
 
212631< 0.1%
 
192121< 0.1%
 
253531< 0.1%
 
274001< 0.1%
 
48711< 0.1%
 
130591< 0.1%
 
151061< 0.1%
 
89611< 0.1%
 
Other values (16937)1693799.9%
 
ValueCountFrequency (%) 
48541< 0.1%
 
48561< 0.1%
 
48581< 0.1%
 
48591< 0.1%
 
48601< 0.1%
 
ValueCountFrequency (%) 
287731< 0.1%
 
287721< 0.1%
 
287711< 0.1%
 
287701< 0.1%
 
287691< 0.1%
 

Region
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
Japan
4892 
EMEA
4664 
Americas
3945 
APAC
3262 
Middle East
 
184
ValueCountFrequency (%) 
Japan489228.9%
 
EMEA466427.5%
 
Americas394523.3%
 
APAC326219.2%
 
Middle East1841.1%
 
2020-10-24T11:33:39.875166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:39.959438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:40.080493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length5
Mean length5.295804567
Min length4

Territory
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct76
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
None
4999 
Germany
1682 
NW America
1568 
Australia
1208 
India
809 
Other values (71)
6681 
ValueCountFrequency (%) 
None499929.5%
 
Germany16829.9%
 
NW America15689.3%
 
Australia12087.1%
 
India8094.8%
 
NE America7604.5%
 
Japan6814.0%
 
Netherlands5333.1%
 
SE America4932.9%
 
France4492.6%
 
Other values (66)376522.2%
 
2020-10-24T11:33:40.212441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)< 0.1%
2020-10-24T11:33:40.346460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length7
Mean length7.018646368
Min length3
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
1
13693 
0
3254 
ValueCountFrequency (%) 
11369380.8%
 
0325419.2%
 
2020-10-24T11:33:40.436223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
1
9890 
0
7057 
ValueCountFrequency (%) 
1989058.4%
 
0705741.6%
 
2020-10-24T11:33:40.483068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
0
8889 
1
8058 
ValueCountFrequency (%) 
0888952.5%
 
1805847.5%
 
2020-10-24T11:33:40.527251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
0
11543 
1
5404 
ValueCountFrequency (%) 
01154368.1%
 
1540431.9%
 
2020-10-24T11:33:40.571212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Submitted_for_Approval
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 KiB
0
16947 
ValueCountFrequency (%) 
016947100.0%
 
2020-10-24T11:33:40.611943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
Bureaucratic_Code_4
12587 
Bureaucratic_Code_5
3803 
Bureaucratic_Code_1
 
261
Bureaucratic_Code_2
 
242
Bureaucratic_Code_0
 
51
Other values (2)
 
3
ValueCountFrequency (%) 
Bureaucratic_Code_41258774.3%
 
Bureaucratic_Code_5380322.4%
 
Bureaucratic_Code_12611.5%
 
Bureaucratic_Code_22421.4%
 
Bureaucratic_Code_0510.3%
 
Bureaucratic_Code_32< 0.1%
 
Bureaucratic_Code_61< 0.1%
 
2020-10-24T11:33:40.687069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-24T11:33:40.768712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:40.905742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19
Distinct809
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
Minimum2013-07-27 00:00:00
Maximum2018-12-21 00:00:00
2020-10-24T11:33:41.008213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:41.115226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

Source
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
None
9497 
Source_7
2965 
Source_9
1459 
Source_11
1421 
Source_13
 
786
Other values (9)
 
819
ValueCountFrequency (%) 
None949756.0%
 
Source_7296517.5%
 
Source_914598.6%
 
Source_1114218.4%
 
Source_137864.6%
 
Source_34182.5%
 
Source_11370.8%
 
Source_101060.6%
 
Source_2860.5%
 
Source_4430.3%
 
Other values (4)290.2%
 
2020-10-24T11:33:41.242395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-24T11:33:41.344975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length4
Mean length5.895025668
Min length4

Billing_Country
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct80
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
Japan
4879 
United States
3802 
Germany
1979 
Australia
1169 
India
672 
Other values (75)
4446 
ValueCountFrequency (%) 
Japan487928.8%
 
United States380222.4%
 
Germany197911.7%
 
Australia11696.9%
 
India6724.0%
 
Netherlands5633.3%
 
Singapore4322.5%
 
France3962.3%
 
Spain3171.9%
 
Italy2951.7%
 
Other values (70)244314.4%
 
2020-10-24T11:33:41.460664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique15 ?
Unique (%)0.1%
2020-10-24T11:33:41.589210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length42
Median length7
Mean length8.187289786
Min length4

Account_Name
Categorical

HIGH CARDINALITY

Distinct1635
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size125.9 KiB
Account_Name_1888
2124 
Account_Name_1836
 
886
Account_Name_25
 
752
Account_Name_1991
 
586
Account_Name_1642
 
397
Other values (1630)
12202 
ValueCountFrequency (%) 
Account_Name_1888212412.5%
 
Account_Name_18368865.2%
 
Account_Name_257524.4%
 
Account_Name_19915863.5%
 
Account_Name_16423972.3%
 
Account_Name_15083552.1%
 
Account_Name_14403171.9%
 
Account_Name_5332831.7%
 
Account_Name_2632491.5%
 
Account_Name_6602311.4%
 
Other values (1625)1076763.5%
 
2020-10-24T11:33:41.725166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique637 ?
Unique (%)3.8%
2020-10-24T11:33:41.842777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length17
Mean length16.51560748
Min length14

Opportunity_Name
Categorical

HIGH CARDINALITY

Distinct9841
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Memory size430.0 KiB
Opportunity_Name_9795
 
36
Opportunity_Name_5036
 
36
Opportunity_Name_12179
 
26
Opportunity_Name_11461
 
26
Opportunity_Name_6835
 
24
Other values (9836)
16799 
ValueCountFrequency (%) 
Opportunity_Name_9795360.2%
 
Opportunity_Name_5036360.2%
 
Opportunity_Name_12179260.2%
 
Opportunity_Name_11461260.2%
 
Opportunity_Name_6835240.1%
 
Opportunity_Name_1661180.1%
 
Opportunity_Name_9628160.1%
 
Opportunity_Name_10946160.1%
 
Opportunity_Name_10945160.1%
 
Opportunity_Name_10944160.1%
 
Other values (9831)1671798.6%
 
2020-10-24T11:33:41.976265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6921 ?
Unique (%)40.8%
2020-10-24T11:33:42.107113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length21
Mean length21.15271139
Min length18

Opportunity_ID
Real number (ℝ≥0)

Distinct9841
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5335.940225
Minimum0
Maximum12803
Zeros1
Zeros (%)< 0.1%
Memory size132.4 KiB
2020-10-24T11:33:42.215599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile435.3
Q12448.5
median5306
Q37698
95-th percentile11203.5
Maximum12803
Range12803
Interquartile range (IQR)5249.5

Descriptive statistics

Standard deviation3324.723809
Coefficient of variation (CV)0.623081157
Kurtosis-0.7150386945
Mean5335.940225
Median Absolute Deviation (MAD)2623
Skewness0.2852975553
Sum90428179
Variance11053788.41
MonotocityIncreasing
2020-10-24T11:33:42.327368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
12460360.2%
 
2008360.2%
 
5123260.2%
 
6606260.2%
 
2911240.1%
 
767180.1%
 
6654160.1%
 
8698160.1%
 
6653160.1%
 
7568160.1%
 
Other values (9831)1671798.6%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
128031< 0.1%
 
128021< 0.1%
 
128013< 0.1%
 
128002< 0.1%
 
127991< 0.1%
 

Sales_Contract_No
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5266
Distinct (%)52.8%
Missing6973
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean2725.414979
Minimum0
Maximum6517
Zeros1
Zeros (%)< 0.1%
Memory size132.4 KiB
2020-10-24T11:33:42.439053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile240.65
Q11396.5
median2801.5
Q34009
95-th percentile5203.35
Maximum6517
Range6517
Interquartile range (IQR)2612.5

Descriptive statistics

Standard deviation1569.252469
Coefficient of variation (CV)0.5757847818
Kurtosis-1.132015109
Mean2725.414979
Median Absolute Deviation (MAD)1285
Skewness-0.0416636759
Sum27183289
Variance2462553.311
MonotocityNot monotonic
2020-10-24T11:33:42.540632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
108360.2%
 
479360.2%
 
2575180.1%
 
3980160.1%
 
3876160.1%
 
3877160.1%
 
3878160.1%
 
3123160.1%
 
5583160.1%
 
4526140.1%
 
Other values (5256)977457.7%
 
(Missing)697341.1%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
25< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
65171< 0.1%
 
65161< 0.1%
 
65152< 0.1%
 
55933< 0.1%
 
55902< 0.1%
 

Account_Owner
Categorical

HIGH CORRELATION

Distinct48
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Person_Name_50
3730 
Person_Name_13
1328 
Person_Name_8
1286 
Person_Name_43
1241 
Person_Name_18
1093 
Other values (43)
8269 
ValueCountFrequency (%) 
Person_Name_50373022.0%
 
Person_Name_1313287.8%
 
Person_Name_812867.6%
 
Person_Name_4312417.3%
 
Person_Name_1810936.4%
 
Person_Name_38755.2%
 
Person_Name_326824.0%
 
Person_Name_46643.9%
 
Person_Name_656313.7%
 
Person_Name_645773.4%
 
Other values (38)484028.6%
 
2020-10-24T11:33:42.669393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-10-24T11:33:42.785638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length13.80090872
Min length13

Opportunity_Owner
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size19.5 KiB
Person_Name_50
3781 
Person_Name_8
1422 
Person_Name_13
1254 
Person_Name_18
982 
Person_Name_43
 
838
Other values (48)
8670 
ValueCountFrequency (%) 
Person_Name_50378122.3%
 
Person_Name_814228.4%
 
Person_Name_1312547.4%
 
Person_Name_189825.8%
 
Person_Name_438384.9%
 
Person_Name_38324.9%
 
Person_Name_46874.1%
 
Person_Name_326864.0%
 
Person_Name_196153.6%
 
Person_Name_645163.0%
 
Other values (43)533431.5%
 
2020-10-24T11:33:42.907513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-24T11:33:43.024506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length13.8094648
Min length13

Account_Type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
Account_Type_2
8832 
Account_Type_0
5868 
Account_Type_5
1943 
Account_Type_1
 
165
None
 
112
Other values (2)
 
27
ValueCountFrequency (%) 
Account_Type_2883252.1%
 
Account_Type_0586834.6%
 
Account_Type_5194311.5%
 
Account_Type_11651.0%
 
None1120.7%
 
Account_Type_6170.1%
 
Account_Type_4100.1%
 
2020-10-24T11:33:43.376936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:43.451737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:43.575920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length13.93391161
Min length4

Opportunity_Type
Categorical

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Opportunity_Type_1
6819 
Opportunity_Type_7
5763 
Opportunity_Type_19
1900 
Opportunity_Type_8
1458 
Opportunity_Type_12
 
279
Other values (20)
728 
ValueCountFrequency (%) 
Opportunity_Type_1681940.2%
 
Opportunity_Type_7576334.0%
 
Opportunity_Type_19190011.2%
 
Opportunity_Type_814588.6%
 
Opportunity_Type_122791.6%
 
Opportunity_Type_32751.6%
 
Opportunity_Type_201751.0%
 
Opportunity_Type_2700.4%
 
Opportunity_Type_4510.3%
 
Opportunity_Type_10340.2%
 
Other values (15)1230.7%
 
2020-10-24T11:33:43.698153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-24T11:33:43.819278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length18
Mean length18.14539447
Min length18

Quote_Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
Non Binding
16777 
Binding
 
170
ValueCountFrequency (%) 
Non Binding1677799.0%
 
Binding1701.0%
 
2020-10-24T11:33:43.927524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:43.997213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:44.082622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.9598749
Min length7

Delivery_Terms
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
Delivery_Terms_4
7712 
Delivery_Terms_2
4633 
Delivery_Terms_1
2936 
Delivery_Terms_6
781 
Delivery_Terms_5
 
636
Other values (4)
 
249
ValueCountFrequency (%) 
Delivery_Terms_4771245.5%
 
Delivery_Terms_2463327.3%
 
Delivery_Terms_1293617.3%
 
Delivery_Terms_67814.6%
 
Delivery_Terms_56363.8%
 
Delivery_Terms_81641.0%
 
Delivery_Terms_3410.2%
 
Delivery_Terms_7380.2%
 
Delivery_Terms_06< 0.1%
 
2020-10-24T11:33:44.205767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:44.290217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:44.428283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length16
Mean length16
Min length16
Distinct1096
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
Minimum2013-11-05 00:00:00
Maximum2018-12-29 00:00:00
2020-10-24T11:33:44.523829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:44.629109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

Brand
Categorical

Distinct26
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
None
15911 
Other
 
607
Brand_9
 
140
Brand_5
 
48
Brand_24
 
38
Other values (21)
 
203
ValueCountFrequency (%) 
None1591193.9%
 
Other6073.6%
 
Brand_91400.8%
 
Brand_5480.3%
 
Brand_24380.2%
 
Brand_11340.2%
 
Brand_25240.1%
 
Brand_10220.1%
 
Brand_22200.1%
 
Brand_7170.1%
 
Other values (16)860.5%
 
2020-10-24T11:33:44.784635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2020-10-24T11:33:44.910989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.12409276
Min length4

Product_Type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
None
15935 
Other
 
515
Product_Type_3
 
179
Product_Type_1
 
142
Product_Type_0
 
94
Other values (2)
 
82
ValueCountFrequency (%) 
None1593594.0%
 
Other5153.0%
 
Product_Type_31791.1%
 
Product_Type_11420.8%
 
Product_Type_0940.6%
 
Product_Type_4730.4%
 
Product_Type_290.1%
 
2020-10-24T11:33:45.035607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:45.117258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:45.241013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length4
Mean length4.323656104
Min length4

Size
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
None
15967 
Other
 
394
Size_3
 
356
Size_4
 
196
Size_1
 
25
Other values (2)
 
9
ValueCountFrequency (%) 
None1596794.2%
 
Other3942.3%
 
Size_33562.1%
 
Size_41961.2%
 
Size_1250.1%
 
Size_07< 0.1%
 
Size_22< 0.1%
 
2020-10-24T11:33:45.359178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:45.442991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:45.584411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.092405736
Min length4
Distinct28
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.0 KiB
None
15928 
Other
 
523
Product_Category_B_2
 
59
Product_Category_B_12
 
51
Product_Category_B_7
 
47
Other values (23)
 
339
ValueCountFrequency (%) 
None1592894.0%
 
Other5233.1%
 
Product_Category_B_2590.3%
 
Product_Category_B_12510.3%
 
Product_Category_B_7470.3%
 
Product_Category_B_4370.2%
 
Product_Category_B_5360.2%
 
Product_Category_B_17270.2%
 
Product_Category_B_1240.1%
 
Product_Category_B_3230.1%
 
Other values (18)1921.1%
 
2020-10-24T11:33:45.744458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:45.873560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length4
Mean length4.512008025
Min length4

Price
Categorical

HIGH CARDINALITY

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
None
15982 
Other
 
609
0.24
 
27
0.41
 
22
0.28
 
20
Other values (48)
 
287
ValueCountFrequency (%) 
None1598294.3%
 
Other6093.6%
 
0.24270.2%
 
0.41220.1%
 
0.28200.1%
 
0.27180.1%
 
0.29170.1%
 
0.32150.1%
 
0.38140.1%
 
0.35140.1%
 
Other values (43)2091.2%
 
2020-10-24T11:33:46.005505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2020-10-24T11:33:46.139958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length4.039062961
Min length3

Currency
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
None
16052 
EUR
 
475
USD
 
420
ValueCountFrequency (%) 
None1605294.7%
 
EUR4752.8%
 
USD4202.5%
 
2020-10-24T11:33:46.274131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:46.357238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:46.447557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.947188293
Min length3

Last_Activity
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
NaT
16947 
ValueCountFrequency (%) 
NaT16947100.0%
 
2020-10-24T11:33:46.556403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:46.623735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:46.693469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct1002
Distinct (%)8.1%
Missing4625
Missing (%)27.3%
Memory size132.4 KiB
Minimum2014-09-30 00:00:00
Maximum2019-06-30 00:00:00
2020-10-24T11:33:46.793402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:46.908060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
Distinct650
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
Minimum2015-03-12 00:00:00
Maximum2018-12-29 00:00:00
2020-10-24T11:33:47.034597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:47.163439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

Last_Modified_By
Categorical

HIGH CARDINALITY

Distinct55
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size19.5 KiB
Person_Name_47
8515 
Person_Name_50
1210 
Person_Name_43
 
587
Person_Name_13
 
578
Person_Name_8
 
511
Other values (50)
5546 
ValueCountFrequency (%) 
Person_Name_47851550.2%
 
Person_Name_5012107.1%
 
Person_Name_435873.5%
 
Person_Name_135783.4%
 
Person_Name_85113.0%
 
Person_Name_184382.6%
 
Person_Name_334292.5%
 
Person_Name_193952.3%
 
Person_Name_33952.3%
 
Person_Name_43151.9%
 
Other values (45)357421.1%
 
2020-10-24T11:33:47.295713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2020-10-24T11:33:47.414050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length14
Mean length13.89809406
Min length13

Product_Family
Categorical

HIGH CARDINALITY

Distinct227
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size44.9 KiB
Product_Family_77
1345 
Product_Family_133
1249 
Product_Family_132
 
763
Product_Family_212
 
636
Product_Family_100
 
619
Other values (222)
12335 
ValueCountFrequency (%) 
Product_Family_7713457.9%
 
Product_Family_13312497.4%
 
Product_Family_1327634.5%
 
Product_Family_2126363.8%
 
Product_Family_1006193.7%
 
Product_Family_855963.5%
 
Product_Family_1155403.2%
 
Product_Family_2265243.1%
 
Product_Family_1094992.9%
 
Product_Family_2084782.8%
 
Other values (217)969857.2%
 
2020-10-24T11:33:47.549370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique29 ?
Unique (%)0.2%
2020-10-24T11:33:47.681001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length18
Mean length17.6441258
Min length16

Product_Name
Categorical

HIGH CARDINALITY

Distinct456
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
Product_Name_100
 
976
Product_Name_226
 
836
Product_Name_231
 
579
Product_Name_491
 
524
Product_Name_4
 
474
Other values (451)
13558 
ValueCountFrequency (%) 
Product_Name_1009765.8%
 
Product_Name_2268364.9%
 
Product_Name_2315793.4%
 
Product_Name_4915243.1%
 
Product_Name_44742.8%
 
Product_Name_1924422.6%
 
Product_Name_1113882.3%
 
Product_Name_1323832.3%
 
Product_Name_4953502.1%
 
Product_Name_293452.0%
 
Other values (446)1165068.7%
 
2020-10-24T11:33:47.821524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique84 ?
Unique (%)0.5%
2020-10-24T11:33:47.967754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length16
Mean length15.74609075
Min length14

ASP_Currency
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
USD
7361 
JPY
4522 
EUR
4503 
AUD
 
556
GBP
 
5
ValueCountFrequency (%) 
USD736143.4%
 
JPY452226.7%
 
EUR450326.6%
 
AUD5563.3%
 
GBP5< 0.1%
 
2020-10-24T11:33:48.116776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:48.204981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:48.327658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

ASP
Real number (ℝ≥0)

MISSING
ZEROS

Distinct301
Distinct (%)2.2%
Missing3209
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean7.012128279
Minimum0
Maximum80
Zeros301
Zeros (%)1.8%
Memory size132.4 KiB
2020-10-24T11:33:48.460703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.36
median0.4
Q30.505
95-th percentile59
Maximum80
Range80
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation18.85835901
Coefficient of variation (CV)2.689391617
Kurtosis5.261237009
Mean7.012128279
Median Absolute Deviation (MAD)0.06
Skewness2.623799882
Sum96332.6183
Variance355.6377046
MonotocityNot monotonic
2020-10-24T11:33:48.623850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
0.388014.7%
 
0.47124.2%
 
0.365643.3%
 
0.425103.0%
 
0.394652.7%
 
0.373652.2%
 
03011.8%
 
0.352951.7%
 
0.342701.6%
 
0.3652691.6%
 
Other values (291)918654.2%
 
(Missing)320918.9%
 
ValueCountFrequency (%) 
03011.8%
 
1e-051< 0.1%
 
0.15530.3%
 
0.168< 0.1%
 
0.171140.7%
 
ValueCountFrequency (%) 
80140.1%
 
78860.5%
 
77120.1%
 
766< 0.1%
 
75990.6%
 

ASP_(converted)_Currency
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 KiB
USD
16947 
ValueCountFrequency (%) 
USD16947100.0%
 
2020-10-24T11:33:48.756972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:48.833155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:48.906121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

ASP_(converted)
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct441
Distinct (%)3.2%
Missing3209
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean0.4325537043
Minimum0
Maximum67
Zeros300
Zeros (%)1.8%
Memory size132.4 KiB
2020-10-24T11:33:49.013879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27
Q10.37326
median0.41285
Q30.47649
95-th percentile0.63
Maximum67
Range67
Interquartile range (IQR)0.10323

Descriptive statistics

Standard deviation0.7586587831
Coefficient of variation (CV)1.753906568
Kurtosis6675.953735
Mean0.4325537043
Median Absolute Deviation (MAD)0.0509
Skewness80.47407411
Sum5942.42279
Variance0.5755631491
MonotocityNot monotonic
2020-10-24T11:33:49.431045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
0.385703.4%
 
0.44972.9%
 
0.363261.9%
 
0.393251.9%
 
03001.8%
 
0.422941.7%
 
0.407192341.4%
 
0.429822301.4%
 
0.475062161.3%
 
0.452442141.3%
 
Other values (431)1053262.1%
 
(Missing)320918.9%
 
ValueCountFrequency (%) 
03001.8%
 
1e-051< 0.1%
 
0.003781< 0.1%
 
0.15530.3%
 
0.168< 0.1%
 
ValueCountFrequency (%) 
671< 0.1%
 
57.685781< 0.1%
 
1.71< 0.1%
 
1.66< 0.1%
 
12< 0.1%
 
Distinct986
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
Minimum2016-01-01 00:00:00
Maximum2020-09-17 00:00:00
2020-10-24T11:33:49.544037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:49.657771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
Distinct1016
Distinct (%)6.0%
Missing75
Missing (%)0.4%
Memory size132.4 KiB
Minimum2016-01-05 00:00:00
Maximum2208-12-31 00:00:00
2020-10-24T11:33:49.783889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:49.905224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

Month
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
2018 - 10
 
666
2017 - 6
 
643
2017 - 10
 
638
2016 - 3
 
628
2017 - 7
 
625
Other values (48)
13747 
ValueCountFrequency (%) 
2018 - 106663.9%
 
2017 - 66433.8%
 
2017 - 106383.8%
 
2016 - 36283.7%
 
2017 - 76253.7%
 
2017 - 55863.5%
 
2018 - 45793.4%
 
2017 - 125743.4%
 
2018 - 65423.2%
 
2018 - 75363.2%
 
Other values (43)1093064.5%
 
2020-10-24T11:33:50.041392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2020-10-24T11:33:50.155482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length8.244527055
Min length8

Delivery_Quarter
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
Q2
4443 
Q1
4317 
Q4
4144 
Q3
4043 
ValueCountFrequency (%) 
Q2444326.2%
 
Q1431725.5%
 
Q4414424.5%
 
Q3404323.9%
 
2020-10-24T11:33:50.270264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:50.352888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:50.450532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Delivery_Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.137428
Minimum2016
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size33.1 KiB
2020-10-24T11:33:50.549028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12016
median2017
Q32018
95-th percentile2018
Maximum2020
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8296028156
Coefficient of variation (CV)0.0004112772902
Kurtosis-0.9481673267
Mean2017.137428
Median Absolute Deviation (MAD)1
Skewness0.03065373257
Sum34184428
Variance0.6882408316
MonotocityNot monotonic
2020-10-24T11:33:50.650519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
2017631837.3%
 
2018583034.4%
 
2016437125.8%
 
20194142.4%
 
2020140.1%
 
ValueCountFrequency (%) 
2016437125.8%
 
2017631837.3%
 
2018583034.4%
 
20194142.4%
 
2020140.1%
 
ValueCountFrequency (%) 
2020140.1%
 
20194142.4%
 
2018583034.4%
 
2017631837.3%
 
2016437125.8%
 

Actual_Delivery_Date
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
NaT
16947 
ValueCountFrequency (%) 
NaT16947100.0%
 
2020-10-24T11:33:50.769855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:50.847490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:50.921234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

TRF
Real number (ℝ≥0)

ZEROS

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.338171948
Minimum0
Maximum500
Zeros11971
Zeros (%)70.6%
Memory size132.4 KiB
2020-10-24T11:33:51.018060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum500
Range500
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.16933936
Coefficient of variation (CV)5.204638339
Kurtosis468.696877
Mean2.338171948
Median Absolute Deviation (MAD)0
Skewness17.060823
Sum39625
Variance148.0928205
MonotocityNot monotonic
2020-10-24T11:33:51.128743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
01197170.6%
 
1219212.9%
 
26273.7%
 
33542.1%
 
52971.8%
 
42081.2%
 
101901.1%
 
61310.8%
 
71110.7%
 
20830.5%
 
Other values (91)7834.6%
 
ValueCountFrequency (%) 
01197170.6%
 
1219212.9%
 
26273.7%
 
33542.1%
 
42081.2%
 
ValueCountFrequency (%) 
5001< 0.1%
 
4291< 0.1%
 
4002< 0.1%
 
2701< 0.1%
 
2501< 0.1%
 

Total_Amount_Currency
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
USD
7361 
JPY
4522 
EUR
4503 
AUD
 
556
GBP
 
5
ValueCountFrequency (%) 
USD736143.4%
 
JPY452226.7%
 
EUR450326.6%
 
AUD5563.3%
 
GBP5< 0.1%
 
2020-10-24T11:33:51.258367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:51.343763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:51.446092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Total_Amount
Real number (ℝ≥0)

SKEWED

Distinct8872
Distinct (%)52.5%
Missing59
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1993659.215
Minimum0
Maximum1320000000
Zeros52
Zeros (%)0.3%
Memory size132.4 KiB
2020-10-24T11:33:51.559504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2900
Q126775
median107250
Q3460389.825
95-th percentile7010372.365
Maximum1320000000
Range1320000000
Interquartile range (IQR)433614.825

Descriptive statistics

Standard deviation18484259.88
Coefficient of variation (CV)9.271524312
Kurtosis2322.422807
Mean1993659.215
Median Absolute Deviation (MAD)101050
Skewness42.00442155
Sum3.366891683e+10
Variance3.416678634e+14
MonotocityNot monotonic
2020-10-24T11:33:51.713612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
62005133.0%
 
44004612.7%
 
180003532.1%
 
1400002721.6%
 
16002041.2%
 
1600001320.8%
 
1096201000.6%
 
23400800.5%
 
85260750.4%
 
81200750.4%
 
Other values (8862)1462386.3%
 
ValueCountFrequency (%) 
0520.3%
 
0.017< 0.1%
 
0.023< 0.1%
 
0.041< 0.1%
 
0.11< 0.1%
 
ValueCountFrequency (%) 
13200000001< 0.1%
 
9199924101< 0.1%
 
7600000001< 0.1%
 
6600052801< 0.1%
 
6600046201< 0.1%
 

Total_Taxable_Amount_Currency
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
USD
7347 
JPY
4525 
EUR
4518 
AUD
 
553
GBP
 
4
ValueCountFrequency (%) 
USD734743.4%
 
JPY452526.7%
 
EUR451826.7%
 
AUD5533.3%
 
GBP4< 0.1%
 
2020-10-24T11:33:51.859277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:51.946188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:52.046858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Total_Taxable_Amount
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct7096
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3224116.789
Minimum0
Maximum1356338620
Zeros997
Zeros (%)5.9%
Memory size132.4 KiB
2020-10-24T11:33:52.147848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q185465.8
median376067.25
Q31037450
95-th percentile12770625
Maximum1356338620
Range1356338620
Interquartile range (IQR)951984.2

Descriptive statistics

Standard deviation20303604.73
Coefficient of variation (CV)6.297416022
Kurtosis1706.197108
Mean3224116.789
Median Absolute Deviation (MAD)310517.25
Skewness33.59918963
Sum5.463910723e+10
Variance4.122363649e+14
MonotocityNot monotonic
2020-10-24T11:33:52.272098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
09975.9%
 
588325720.4%
 
109620680.4%
 
488700660.4%
 
413700600.4%
 
552400600.4%
 
1767640480.3%
 
523100400.2%
 
105840370.2%
 
31053609.6360.2%
 
Other values (7086)1546391.2%
 
ValueCountFrequency (%) 
09975.9%
 
0.151< 0.1%
 
451< 0.1%
 
100.11< 0.1%
 
101.42< 0.1%
 
ValueCountFrequency (%) 
13563386201< 0.1%
 
9199924101< 0.1%
 
7600000001< 0.1%
 
6600052801< 0.1%
 
6600046201< 0.1%
 

Stage
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
Closed Won
9533 
Closed Lost
7350 
Proposal
 
35
Negotiation
 
18
Qualification
 
11
ValueCountFrequency (%) 
Closed Won953356.3%
 
Closed Lost735043.4%
 
Proposal350.2%
 
Negotiation180.1%
 
Qualification110.1%
 
2020-10-24T11:33:52.401172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:52.475072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:52.585382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length10
Mean length10.43258394
Min length8

Prod_Category_A
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.4 KiB
Prod_Category_A_None
16947 
ValueCountFrequency (%) 
Prod_Category_A_None16947100.0%
 
2020-10-24T11:33:52.718232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-24T11:33:52.786616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:52.863726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length20
Mean length20
Min length20

Interactions

2020-10-24T11:33:21.991697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:22.196636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:22.411286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:22.626624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:22.859253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:23.060942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:23.214560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:23.342506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:23.487322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:23.625101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:23.761991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:23.894647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:24.026434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:24.159928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:24.289524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.081602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.192270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.318716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.443097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.555970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.675326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.794762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:25.917344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.038919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.161517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.277541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.401605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.525933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.642483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.765698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:26.891112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.018885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.143730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.272301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.388692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.545914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.678390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.799368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:27.927826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.056882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.206991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.336734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.465309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.585371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.722182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.855173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:28.975378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:29.227492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:29.358028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:29.493186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:29.622278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:29.752741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:29.871761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.007629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.140523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.248042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.361500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.475679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.593048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.712060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.831062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:30.939075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.061550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.182233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.311414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.446431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.582059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.723108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.860890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:31.999659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:32.126427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:32.269434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:32.410652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:32.535725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:32.662986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:32.794119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:32.927501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:33.057748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:33.189218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:33.311121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:33.446332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-24T11:33:52.976458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-24T11:33:53.258093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-24T11:33:53.535712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-24T11:33:53.906604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-24T11:33:54.349398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-24T11:33:34.020101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:37.550796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:38.755666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-24T11:33:39.021606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

IDRegionTerritoryPricing, Delivery_Terms_Quote_ApprPricing, Delivery_Terms_ApprovedBureaucratic_Code_0_ApprovalBureaucratic_Code_0_ApprovedSubmitted_for_ApprovalBureaucratic_CodeAccount_Created_DateSourceBilling_CountryAccount_NameOpportunity_NameOpportunity_IDSales_Contract_NoAccount_OwnerOpportunity_OwnerAccount_TypeOpportunity_TypeQuote_TypeDelivery_TermsOpportunity_Created_DateBrandProduct_TypeSizeProduct_Category_BPriceCurrencyLast_ActivityQuote_Expiry_DateLast_Modified_DateLast_Modified_ByProduct_FamilyProduct_NameASP_CurrencyASPASP_(converted)_CurrencyASP_(converted)Planned_Delivery_Start_DatePlanned_Delivery_End_DateMonthDelivery_QuarterDelivery_YearActual_Delivery_DateTRFTotal_Amount_CurrencyTotal_AmountTotal_Taxable_Amount_CurrencyTotal_Taxable_AmountStageProd_Category_A
027761EMEANone11110Bureaucratic_Code_42015-06-16NoneNetherlandsAccount_Name_619Opportunity_Name_125980nanPerson_Name_51Person_Name_18Account_Type_2Opportunity_Type_1Non BindingDelivery_Terms_22015-12-07NoneNoneNoneNoneNoneNoneNaT2016-01-302016-06-13Person_Name_18Product_Family_77Product_Name_99EUR0.52USD0.592016-05-012016-06-302016 - 5Q22016NaT10EUR5,272,800.00EUR5,272,800.00Closed LostProd_Category_A_None
127760EMEANone00000Bureaucratic_Code_42015-06-16NoneNetherlandsAccount_Name_619Opportunity_Name_12600139.00Person_Name_51Person_Name_20Account_Type_2Opportunity_Type_1Non BindingDelivery_Terms_22015-12-07NoneNoneNoneNoneNoneNoneNaT2016-01-202016-01-15Person_Name_20Product_Family_77Product_Name_100EUR0.53USD0.602016-01-182016-01-202016 - 1Q12016NaT0EUR48,230.00EUR48,230.00Closed WonProd_Category_A_None
227446AmericasNW America00000Bureaucratic_Code_42015-04-21Source_7United StatesAccount_Name_1794Opportunity_Name_4692760.00Person_Name_64Person_Name_8Account_Type_5Opportunity_Type_1Non BindingDelivery_Terms_42015-12-08NoneNoneNoneNoneNoneNoneNaT2016-09-302016-09-29Person_Name_8Product_Family_81Product_Name_91USD0.48USD0.482016-01-252016-01-252016 - 1Q12016NaT0USD83,865.60USD83,865.60Closed WonProd_Category_A_None
316808AmericasNW America10100Bureaucratic_Code_52013-07-27Source_11United StatesAccount_Name_1201Opportunity_Name_4153nanPerson_Name_8Person_Name_8Account_Type_5Opportunity_Type_19Non BindingDelivery_Terms_12015-12-08OtherProduct_Type_0Size_4Product_Category_B_160.42USDNaTNaT2018-03-27Person_Name_8Product_Family_209Product_Name_432USD0.53USD0.532018-02-012018-03-312018 - 2Q12018NaT14USD7,421,881.50USD7,421,881.50Closed LostProd_Category_A_None
416805AmericasNW America10100Bureaucratic_Code_52013-07-27Source_11United StatesAccount_Name_1201Opportunity_Name_8514nanPerson_Name_8Person_Name_8Account_Type_5Opportunity_Type_19Non BindingDelivery_Terms_12015-12-08OtherProduct_Type_0Size_4Product_Category_B_160.42USDNaT2017-06-022018-03-27Person_Name_8Product_Family_209Product_Name_432USD0.53USD0.532018-02-012018-02-282018 - 2Q12018NaT25USD13,357,192.50USD13,357,192.50Closed LostProd_Category_A_None
516802AmericasNW America10100Bureaucratic_Code_52013-07-27Source_11United StatesAccount_Name_1201Opportunity_Name_9395nanPerson_Name_8Person_Name_8Account_Type_5Opportunity_Type_19Non BindingDelivery_Terms_12015-12-08OtherProduct_Type_0Size_4Product_Category_B_160.42USDNaT2017-06-022018-03-27Person_Name_8Product_Family_209Product_Name_432USD0.53USD0.532018-02-012018-03-312018 - 2Q12018NaT28USD14,838,277.50USD14,838,277.50Closed LostProd_Category_A_None
616799AmericasNW America10100Bureaucratic_Code_52013-07-27Source_11United StatesAccount_Name_1201Opportunity_Name_16186nanPerson_Name_8Person_Name_8Account_Type_5Opportunity_Type_19Non BindingDelivery_Terms_42015-12-08NoneNoneNoneNoneNoneNoneNaTNaT2016-10-07Person_Name_8Product_Family_164Product_Name_308USD0.38USD0.382017-02-012017-05-012017 - 2Q12017NaT7USD2,659,494.60USD2,659,494.60Closed LostProd_Category_A_None
727455AmericasNW America11110Bureaucratic_Code_42015-04-21Source_7United StatesAccount_Name_1794Opportunity_Name_82277nanPerson_Name_64Person_Name_8Account_Type_5Opportunity_Type_1Non BindingDelivery_Terms_42015-12-09NoneNoneNoneNoneNoneNoneNaT2016-01-072015-12-09Person_Name_8Product_Family_143Product_Name_251USD0.48USD0.482016-01-252016-01-252016 - 1Q12016NaT0USD50,688.00USD50,688.00Closed WonProd_Category_A_None
824353JapanNone10000Bureaucratic_Code_52015-04-20NoneJapanAccount_Name_1888Opportunity_Name_61518nanPerson_Name_50Person_Name_50Account_Type_2Opportunity_Type_7Non BindingDelivery_Terms_42015-12-09NoneNoneNoneNoneNoneNoneNaTNaT2016-04-05Person_Name_50Product_Family_6Product_Name_6JPYnanUSDnan2016-02-292016-02-292016 - 2Q12016NaT0JPY15,600.00JPY470,400.00Closed LostProd_Category_A_None
924355JapanNone10000Bureaucratic_Code_52015-04-20NoneJapanAccount_Name_1888Opportunity_Name_61518nanPerson_Name_50Person_Name_50Account_Type_2Opportunity_Type_7Non BindingDelivery_Terms_42015-12-09NoneNoneNoneNoneNoneNoneNaTNaT2016-04-05Person_Name_50Product_Family_4Product_Name_4JPYnanUSDnan2016-02-292016-02-292016 - 2Q12016NaT0JPY4,400.00JPY470,400.00Closed LostProd_Category_A_None

Last rows

IDRegionTerritoryPricing, Delivery_Terms_Quote_ApprPricing, Delivery_Terms_ApprovedBureaucratic_Code_0_ApprovalBureaucratic_Code_0_ApprovedSubmitted_for_ApprovalBureaucratic_CodeAccount_Created_DateSourceBilling_CountryAccount_NameOpportunity_NameOpportunity_IDSales_Contract_NoAccount_OwnerOpportunity_OwnerAccount_TypeOpportunity_TypeQuote_TypeDelivery_TermsOpportunity_Created_DateBrandProduct_TypeSizeProduct_Category_BPriceCurrencyLast_ActivityQuote_Expiry_DateLast_Modified_DateLast_Modified_ByProduct_FamilyProduct_NameASP_CurrencyASPASP_(converted)_CurrencyASP_(converted)Planned_Delivery_Start_DatePlanned_Delivery_End_DateMonthDelivery_QuarterDelivery_YearActual_Delivery_DateTRFTotal_Amount_CurrencyTotal_AmountTotal_Taxable_Amount_CurrencyTotal_Taxable_AmountStageProd_Category_A
1693717683EMEAGermany00000Bureaucratic_Code_42013-07-27Source_9GermanyAccount_Name_533Opportunity_Name_406012797nanPerson_Name_13Person_Name_13Account_Type_0Opportunity_Type_8Non BindingDelivery_Terms_22015-12-04NoneNoneNoneNoneNoneNoneNaT2016-01-032015-12-18Person_Name_13Product_Family_77Product_Name_96EUR0.53USD0.602016-02-012016-02-292016 - 2Q12016NaT1EUR413,400.00EUR528,918.75Closed WonProd_Category_A_None
1693828767EMEAGermany10000Bureaucratic_Code_52015-12-04NoneGermanyAccount_Name_586Opportunity_Name_508812798nanPerson_Name_13Person_Name_13Account_Type_2Opportunity_Type_19Non BindingDelivery_Terms_22015-12-04NoneNoneNoneNoneNoneNoneNaT2016-02-052016-10-21Person_Name_13Product_Family_85Product_Name_110EUR0.47USD0.532016-08-152016-09-302016 - 8Q32016NaT3EUR1,307,775.00EUR1,307,775.00Closed LostProd_Category_A_None
1693918324EMEANorway11000Bureaucratic_Code_42013-07-27Source_9GermanyAccount_Name_533Opportunity_Name_720212799224.00Person_Name_13Person_Name_13Account_Type_0Opportunity_Type_1Non BindingDelivery_Terms_22015-12-04NoneNoneNoneNoneNoneNoneNaT2016-04-032016-04-01Person_Name_13Product_Family_77Product_Name_96EUR0.52USD0.582016-04-012016-04-212016 - 4Q22016NaT1EUR401,700.00EUR401,700.00Closed WonProd_Category_A_None
1694020827AmericasNE America10000Bureaucratic_Code_52014-06-16NoneGermanyAccount_Name_404Opportunity_Name_1197912800nanPerson_Name_13Person_Name_13Account_Type_5Opportunity_Type_19Non BindingDelivery_Terms_42015-12-04NoneNoneNoneNoneNoneNoneNaT2016-01-152016-10-10Person_Name_13Product_Family_164Product_Name_307USD0.53USD0.532016-10-032016-12-302016 - 10Q42016NaT20USD10,751,580.00USD21,332,500.00Closed LostProd_Category_A_None
1694120830AmericasNE America10000Bureaucratic_Code_52014-06-16NoneGermanyAccount_Name_404Opportunity_Name_1197912800nanPerson_Name_13Person_Name_13Account_Type_5Opportunity_Type_19Non BindingDelivery_Terms_42015-12-04NoneNoneNoneNoneNoneNoneNaT2016-01-152016-10-10Person_Name_13Product_Family_158Product_Name_286USD0.53USD0.532016-10-032016-12-302016 - 10Q42016NaT20USD10,580,920.00USD21,332,500.00Closed LostProd_Category_A_None
169428781EMEAAustria11110Bureaucratic_Code_42016-01-15Source_7AustriaAccount_Name_726Opportunity_Name_92451280144.00Person_Name_13Person_Name_13Account_Type_5Opportunity_Type_1Non BindingDelivery_Terms_22015-12-04NoneNoneNoneNoneNoneNoneNaTNaT2016-01-19Person_Name_13Product_Family_85Product_Name_111EUR0.52USD0.592016-03-212016-03-252016 - 3Q12016NaT0EUR103,350.00EUR299,715.00Closed WonProd_Category_A_None
169438786EMEAAustria11110Bureaucratic_Code_42016-01-15Source_7AustriaAccount_Name_726Opportunity_Name_92451280144.00Person_Name_13Person_Name_13Account_Type_5Opportunity_Type_1Non BindingDelivery_Terms_22015-12-04NoneNoneNoneNoneNoneNoneNaTNaT2016-01-19Person_Name_13Product_Family_85Product_Name_111EUR0.52USD0.592016-04-042016-04-082016 - 4Q22016NaT0EUR93,015.00EUR299,715.00Closed WonProd_Category_A_None
169448792EMEAAustria11110Bureaucratic_Code_42016-01-15Source_7AustriaAccount_Name_726Opportunity_Name_92451280144.00Person_Name_13Person_Name_13Account_Type_5Opportunity_Type_1Non BindingDelivery_Terms_22015-12-04NoneNoneNoneNoneNoneNoneNaTNaT2016-01-19Person_Name_13Product_Family_85Product_Name_111EUR0.52USD0.592016-03-282016-03-312016 - 3Q12016NaT0EUR103,350.00EUR299,715.00Closed WonProd_Category_A_None
1694528561AmericasNE America11110Bureaucratic_Code_42015-10-20NoneUnited StatesAccount_Name_944Opportunity_Name_584312802nanPerson_Name_3Person_Name_3Account_Type_5Opportunity_Type_1Non BindingDelivery_Terms_42015-12-05NoneNoneNoneNoneNoneNoneNaT2016-02-292016-01-22Person_Name_3Product_Family_158Product_Name_287USD0.64USD0.642016-04-252016-04-292016 - 4Q22016NaT4USD2,346,796.88USD0.00Closed LostProd_Category_A_None
1694628318AmericasNE America11110Bureaucratic_Code_42015-09-03NoneUnited StatesAccount_Name_1401Opportunity_Name_991112803nanPerson_Name_3Person_Name_3Account_Type_5Opportunity_Type_19Non BindingDelivery_Terms_42015-12-05NoneNoneNoneNoneNoneNoneNaT2016-01-062016-09-28Person_Name_3Product_Family_164Product_Name_307USD0.64USD0.642016-07-012016-08-312016 - 7Q32016NaT40USD25,603,200.00USD0.00Closed LostProd_Category_A_None